knitr::opts_chunk$set(echo = FALSE, message = FALSE)
library(Seurat)
library(ggplot2)
library(data.table)
library(MAST)
library(SingleR)
library(dplyr)
library(tidyr)
library(limma)
library(scRNAseq)

Data Intro and Initial Processing

## Warning: Using `as.character()` on a quosure is deprecated as of rlang 0.3.0.
## Please use `as_label()` or `as_name()` instead.
## This warning is displayed once per session.

Introduction

Things that were brought up last meeting:

What are the MEP/Mast cells, are these really MEPs?

  1. Look at Krause markers?
  2. Look at markers for MEPs?
  3. Coexpression of different markers
  4. How can we isolate these cells?

Krause Markers

## Warning in FetchData(object = object, vars = features, cells = cells): The
## following requested variables were not found: Ahspp

## Warning in FetchData(object = object, vars = features, cells = cells): The
## following requested variables were not found: C3orf58

## Warning in FetchData(object = object, vars = features, cells = cells): The
## following requested variables were not found: C6orf25

Markers of Interest

MEP Markers

Other markers that I have from priya and other sources.

Neither of these was useful

Coexpression of different markers

Looking at some of the top hits (Mcpt8, Prss34) and other MEP genes (Gata2, Runx1, Cpa3), to see how they overlap

##            
##             Itga2b = 0 Itga2b > 0
##   Mcpt8 = 0          3          6
##   Mcpt8 > 0         39        544

##             
##              Itga2b = 0 Itga2b > 0
##   Prss34 = 0         11         18
##   Prss34 > 0         31        532

##            
##             Itga2b = 0 Itga2b > 0
##   Gata2 = 0          4          3
##   Gata2 > 0         38        547

##            
##             Itga2b = 0 Itga2b > 0
##   Runx1 = 0         10          8
##   Runx1 > 0         32        542

##           
##            Itga2b = 0 Itga2b > 0
##   Cpa3 = 0         12        125
##   Cpa3 > 0         30        425

##             
##              Mcpt8 = 0 Mcpt8 > 0
##   Prss34 = 0         8        21
##   Prss34 > 0         1       562

##            
##             Mcpt8 = 0 Mcpt8 > 0
##   Gata2 = 0         2         5
##   Gata2 > 0         7       578

##            
##             Mcpt8 = 0 Mcpt8 > 0
##   Runx1 = 0         5        13
##   Runx1 > 0         4       570

##           
##            Mcpt8 = 0 Mcpt8 > 0
##   Cpa3 = 0         2       135
##   Cpa3 > 0         7       448

##            
##             Prss34 = 0 Prss34 > 0
##   Gata2 = 0          3          4
##   Gata2 > 0         26        559

##            
##             Prss34 = 0 Prss34 > 0
##   Runx1 = 0          9          9
##   Runx1 > 0         20        554

##           
##            Prss34 = 0 Prss34 > 0
##   Cpa3 = 0          5        132
##   Cpa3 > 0         24        431

##            
##             Gata2 = 0 Gata2 > 0
##   Runx1 = 0         4        14
##   Runx1 > 0         3       571

##           
##            Gata2 = 0 Gata2 > 0
##   Cpa3 = 0         6       131
##   Cpa3 > 0         1       454

##           
##            Runx1 = 0 Runx1 > 0
##   Cpa3 = 0         8       129
##   Cpa3 > 0        10       445

How to select this MEP/MCP population

First Step

Most of these cells are coming from the enriched Mpl experiment so I would start there.

Second Step

##                 p_val avg_logFC pct.1 pct.2     p_val_adj
## Cd200r3 1.222623e-253  2.382079 0.990 0.032 2.214293e-249
## Cyp11a1 1.060851e-252  2.902921 0.998 0.049 1.921307e-248
## Gata2   1.018616e-244  2.346238 0.990 0.070 1.844815e-240
## Cd200r4 7.162088e-242  1.849539 0.980 0.059 1.297126e-237
## Il1rl1  7.425986e-240  2.046625 0.972 0.050 1.344920e-235
## Padi2   5.276162e-239  1.809938 0.968 0.045 9.555657e-235